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dc.contributor.advisorBremdal, Bernt Arild
dc.contributor.authorGranmo, Gisle Jaran
dc.date.accessioned2021-12-06T08:56:14Z
dc.date.available2021-12-06T08:56:14Z
dc.date.issued2021-05-18en
dc.description.abstractThis thesis will explore interaction between non-supervised machinelearning agents, with a focus on traffic situations. We intend to showthat self-interested agents can arrive at mutually beneficial strategiesaccording to general game theory, achieving pure or mixed Nash equi-libria, and that this has practical applications for unmanned vehicles.We will start by verifying against simple one stage models, specifi-cally “battle of the sexes” and “chicken race”, before extending this touncontrolled four way intersections.en_US
dc.identifier.urihttps://hdl.handle.net/10037/23273
dc.language.isoengen_US
dc.publisherUiT Norges arktiske universitetno
dc.publisherUiT The Arctic University of Norwayen
dc.rights.holderCopyright 2021 The Author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-sa/4.0en_US
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)en_US
dc.subject.courseIDDTE-3900
dc.subjectVDP::Mathematics and natural science: 400::Information and communication science: 420::Algorithms and computability theory: 422en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Algoritmer og beregnbarhetsteori: 422en_US
dc.titleLearning social codes for self-interested agentsen_US
dc.typeMaster thesisen
dc.typeMastergradsoppgaveno


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Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)